In case you are unfamiliar with Karpathy's Loop[1], it is a genetic algorithm[2] where the genetic "mutations" are clever-but-random ideas generated by an LLM agent, aimed at improving a system.

  (1) Let the LLM randomly perturbate the system.
  (2) Measure the system's performance.
  (3a) If the perturbation improved performance, keep the change.
  (3b) Otherwise, don't.
  (4) Repeat

[1] https://github.com/karpathy/autoresearch

[2] https://en.wikipedia.org/wiki/Genetic_algorithm

Salient on the value of the verifier. Matches my experience in the last two quarters.

Nice detail on the encountered failures. Very similar experiences with my own loops against testsuites.

Great post. A snapshot in time.

Extremely interesting but I don't understand why it was written by an LLM. Either the frontier models are far better than I realized or else writing this document required a lot of manual work regardless at which point why not keep it in your own voice?

> The agent did not know that would also halve the LUT count. It found out by doing it and watching the synthesizer.

So I guess this is an example of an LLM anthropomorphizing and making wild conjectures about the internal workings of a different LLM.

> "If you can write the rules down, an agent will satisfy them faster than your team will."

a fantastic opportunity to become the next next big thing and write a verifier verifier.

at the hypothesized inflexion point where AI instantly performs exactly as commanded, what happens to heavily regulated industries like medical? do we get huge leaps and bounds everywhere EXCEPT where it matters, or is regulation going to be handed over to a verifier verifier?